A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Abstract

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

“…Mohamed et al introduced a bio-inspired convolutional neural network architecture that effectively utilized RNA-seq data for automatic breast cancer detection and classification. This innovative approach outperformed traditional methods, offering promising potential for improving breast cancer diagnosis [ 31 ].…”

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

“…As the volume and complexity of gene expression data burgeon, ML offers a suite of algorithms capable of deciphering intricate patterns within the data [ 12 ]. Studies have utilized various ML approaches, such as decision trees (DT) [ 13 ], neural networks (NN) [ 14 ], support vector machines (SVM) [ 15 ], logistic regression (LR) [ 16 ], and random forests (RF) [ 17 ], alongside more recent innovations in deep learning (DL) [ 18 , 19 ] and ensemble learning [ 20 , 21 ] methods such as extreme gradient boosting (XGBoost) [ 5 ] and adaptive boosting (AdaBoost) [ 22 ], to identify significant biomarkers in BC.…”

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

“…CNNs have emerged as a pivotal tool for classifying gene expression data, thanks to their autonomous feature-learning capability, which curtails the need for manual intervention in high-dimensional genomic data extraction [5][6][7]. By recognizing patterns effectively, they capture both local and global spatial hierarchies of gene expression profiles, a key aspect in identifying complex biological states.…”